Prism - A novel approach to dead body cooling and its parameters

Detrimental effects of scene manipulations on temperature-based time since death estimation

In forensic casework, time since death (TSD) estimations may play a crucial role to establish chains of events as well as for alibi assessment in homicide cases. Classical TSD estimation relies on reasonably stable ambient temperatures and a correct documentation of ambient and rectal temperatures. This constancy is in some cases disturbed by post-discovery alterations of the crime scene, e.g. opening a window. In order to develop a better understanding of this alteration-based detrimental impact on TSD estimation as well as to identify feasible recommendations for casework, the present pilot study examined ambient temperature effects of different window opening scenarios regarding various time intervals (5 to 360 min) in a furnished 10 m2 apartment during winter. In this context, in addition to the ambient temperature and thus the cooling rate of the room, re-approximation to initial room temperature, potential influences on a nomogram-based time since death estimation using a fictitious case, and the impact of the measurement height above the ground were investigated. Our data indicate a significant reduction of the mean temperature decrease rate after 15 min regardless of the remaining opening time and a correlation with the size of the respective opening surfaces. Re-approximation to initial room temperatures was observed with up to three times longer than the initial opening time. There was no evidence of a substantial advantage of temperature measurements above the level of the corpse (> 0.1 m). The limitations of the study and its applicability for forensic casework are critically reviewed.

Hypothermic sepsis in time since death estimation - a case report

Both hyper- and hypothermia are problematic in temperature based forensic time since death estimation. Hyperthermia may occur in infection, traumatic brain injury, and intoxication. Hypothermia is encountered predominantly in exposure. Sepsis may present itself clinically as hypothermic. Sepsis is not uncommon in the forensic setting and mostly occurs in the context of malpractice accusations. There is usually little overlap between sepsis and typical forensic time since death estimation scenarios of violent or otherwise suspicious deaths. In the presented case, hypothermia and time since death estimations did collide. An inmate was found dead in his jail cell. Wardens claimed they had visually approached him alive relatively shortly prior. Rectal temperature measurements, using two separate crime scene thermometers as well as temperature loggers, revealed low rectal temperature at relatively high ambient temperature. These findings suggested a much longer postmortem interval and consequently raised doubts about the stated timeline. The wardens' claims were however confirmed by camera recordings, which also allowed a reasonable estimate of the true time of death. The cause of death was confirmed as septic organ failure at autopsy, which explained low rectal temperature. The presence of WISCHNEWSKI-spots was noted. When the PRISM-method was applied to the temperature recordings, low rectal temperature at the time of death was detected successfully. However, adaptation of the underlying equation for lower "starting temperature" did not produce satisfactory results. It is concluded that even though hypothermia at the time of death may possibly be detected from temperature data, attempts at time since death estimation for cases of hypothermia by adaptation of the equation should be avoided.

Beyond Henssge’s Formula: Using Regression Trees and a Support Vector Machine for Time of Death Estimation in Forensic Medicine

Henssge’s nomogram is a commonly used method to estimate the time of death. However, uncertainties arising from the graphical solution of the original mathematical formula affect the accuracy of the resulting time interval. Using existing machine learning techniques/tools such as support vector machines (SVMs) and decision trees, we present a more accurate and adaptive method for estimating the time of death compared to Henssge’s nomogram. Using the Python programming language, we built a synthetic data-driven model in which the majority of the selected tools can estimate the time of death with low error rates even despite having only 3000 training cases. An SVM with a radial basis function (RBF) kernel and AdaBoost+SVR provided the best results in estimating the time of death with the lowest error with an estimated time of death accuracy of approximately ±20 min or ±9.6 min, respectively, depending on the SVM parameters. The error in the predicted time (tp[h]) was tp±0.7 h with a 94.45% confidence interval. Because training requires only a small quantity of data, our model can be easily customized to specific populations with varied anthropometric parameters or living in different climatic zones. The errors produced by the proposed method are a magnitude smaller than any previous result.

Core body temperatures during final stages of life-an evaluation of data from in-hospital decedents

Temperature-based methods are widely accepted as the gold standard for death time estimation. In the absence of any other information, the nomogram method generally assumes that a person died with a core body temperature of approximately 37.2 °C. Nevertheless, several external and internal factors may alter the body temperature during agony. A retrospective medical record analysis was carried out on in-hospital death cases from two consecutive years of surgical intensive care units to determine the effects of factors influencing the core body temperature at the point of death. Data from 103 case files were included in the statistical data evaluation. The body temperature fluctuated between and within individuals over time. No clear correlation to certain death groups was observed. Even primary cardiac deaths showed broad intervals of temperatures at the point of death. Men seem to die with higher body temperatures than women. The presented data highlight potential biases for death time estimations when generally assuming a core body temperature of 37.2 °C. In conclusion, the estimation of the time of death should include various methods, including a non-temperature-dependent method. Any uncertainties regarding the body temperature at point of death need to be resolved (e.g. by identifying fever constellations) and elucidated if elimination is not possible.